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Found 5 papers, 2 papers with code
Motif-Based Prompt Learning for Universal Cross-Domain Recommendation
By unifying pre-training and recommendation tasks as a common motif-based similarity learning task and integrating adaptable prompt parameters to guide the model in downstream recommendation tasks, MOP excels in transferring domain knowledge effectively.
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation
In terms of the pretext task, in addition to considering the intra-correlations of users and items by the embedding reconstruction task, we add embedding contrastive learning task to capture inter-correlations of users and items.
Self-supervised Graph Learning for Occasional Group Recommendation
As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high-quality group representations.
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models.
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation
Cold-start problem is a fundamental challenge for recommendation tasks.
